Hello Everyone! I have a dataset with 150.000 statistical units (subjects) and 5 variables: - Binary outcome (0/1) (y) - municipality (string) (25 small areas) - gender - age - copper concentration (in ppm) (25 level, one by municipality) The last one, i.e. copper concentration, has been revealed per municipality (25 levels) and it is defined as municipalty mean of the different municipal sampling sites. I'm interested to the (conditional) copper effect on outcome and I have tried to specify this GLMM: gLMM <- glmer (y ~ gender + age + copper + (1 | municipality), family=" binomial", data=datiSM) Is it correct fit a model containing both disaggregated and aggregated variables? Unfortunately, I cannot measure the copper at disaggregated level (by subject). Thanks in advance Davide
model building
2 messages · Davide Guido, Thierry Onkelinx
Dear Davide, Your model formulation is OK. Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey 2015-10-29 20:25 GMT+01:00 Davide Guido via R-sig-mixed-models < r-sig-mixed-models at r-project.org>:
Hello Everyone! I have a dataset with 150.000 statistical units (subjects) and 5 variables: - Binary outcome (0/1) (y) - municipality (string) (25 small areas) - gender - age - copper concentration (in ppm) (25 level, one by municipality) The last one, i.e. copper concentration, has been revealed per municipality (25 levels) and it is defined as municipalty mean of the different municipal sampling sites. I'm interested to the (conditional) copper effect on outcome and I have tried to specify this GLMM: gLMM <- glmer (y ~ gender + age + copper + (1 | municipality), family=" binomial", data=datiSM) Is it correct fit a model containing both disaggregated and aggregated variables? Unfortunately, I cannot measure the copper at disaggregated level (by subject). Thanks in advance Davide
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